necessarily impede rationality but can provide a stabilizing force when balanced with a capacity to adapt. By blending bounded and unbounded rationality, we can create a decision-making model that is both flexible and resilient, accommodating uncertainty while making use of consistent principles that guide human behaviour. This is particularly important when addressing challenges that span decades, such as climate change, resource management, and infrastructure investments, where static assumptions often fail to capture the dynamic interplay of risks, opportunities, and evolving values. For example, reflecting a bounded rationality perspective, Magni 72 pointed out that in real-life applications, decision-makers often use a subjective hurdle rate instead of the normatively suggested cost of capital (e.g., based on models like CAPM or WACC). As discussed, the reliance on WACC and CAPM to estimate discount rates can create a practical circularity. In practice, this simultaneity is usually resolved through assumptions or heuristics, but it underscores the bounded rationality character of capital allocation 73 . Magni critiques conventional NPV for implicitly assuming unbounded rationality and proposes that subjective hurdle rates, while deviating from standards of rational optimisation, reflect real-world choice behaviours. These can be seen as biased from a classical perspective, but within the bounded rationality 74 framework heuristics may often prove useful in decision-making. An alternative approach could be to include non-linear discounting that is based on polynomial damage functions, which are aligned to specific tipping points of climate change that will lead to irreversible damage. These could include for example, the collapse of critical ice sheets, deforestation within the Amazon rainforest and ocean circulation and temperature. This could be expressed as:
D(T) = a ⋅ T + b ⋅ T 2 + c ⋅ T 3 ...
Where: • D(T) represents the economic damage as a function of temperature T (degrees Celsius). • a, b , and c are coefficients that define the sensitivity of economic damage to temperature. • T is the increase in global mean temperature relative to pre-industrial levels. In this model the linear term a would capture early-stage damages, whereas the quadratic/cubic and beyond functions could capture the exponential growth of damages. Elasticity functions could be also incorporated, and costs/damages included within NPV calculations accordingly. Stochastic overlays employing probabilistic methods to account for uncertainty could also be applied. These models have been put forward by multiple academics, including but not limited to Nordhaus (2017) 75 , Stern (2007) 76 and Weitzman (1998) 77 . 72 Magni (2009) Investment decisions, net present value and bounded rationality, Quantitative Finance, Vol. 9, No. 8, December 2009, 967–979 73 See also: Kahneman, D. and Tversky, A. 1982. On the study of statistical intuitions. Cognition , 11 (2), pp.123-141.and Tversky, A. and Kahneman, D., 1974. Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science , 185 (4157), pp.1124-1131. 74 Bounded rationality is the idea that rationality is limited when individuals make decisions, and under these limitations, rational individuals will select a decision that is satisfactory rather than optimal. See: Gigerenzer, G. and Todd, P.M., 1999. Fast and frugal heuristics: The adaptive toolbox. In Simple heuristics that make us smart (pp. 3-34). Oxford University Press. 75 Nordhaus, W.D., 2017. Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences , 114 (7), pp.1518-1523. 76 Stern, N.H., 2007. The economics of climate change: the Stern review . Cambridge University press. 77 Weitzman, M.L., 1998. Recombinant growth. The quarterly journal of economics , 113 (2), pp.331-360.
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